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embedding.py

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+import os
+import pandas
+import dotenv
+import requests
+
+dotenv.load_dotenv(dotenv.find_dotenv())
+HF_TOKEN = os.environ['YOUR_TOKEN']
+
+def query(api_url, headers, texts):
+    response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
+    return response.json()
+
+def main():
+    model_id = "sentence-transformers/all-MiniLM-L6-v2"
+    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    texts = ["How do I get a replacement Medicare card?",
+        "What is the monthly premium for Medicare Part B?",
+        "How do I terminate my Medicare Part B (medical insurance)?",
+        "How do I sign up for Medicare?",
+        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
+        "How do I sign up for Medicare Part B if I already have Part A?",
+        "What are Medicare late enrollment penalties?",
+        "What is Medicare and who can get it?",
+        "How can I get help with my Medicare Part A and Part B premiums?",
+        "What are the different parts of Medicare?",
+        "Will my Medicare premiums be higher because of my higher income?",
+        "What is TRICARE ?",
+        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]
+
+    output = query(api_url, headers, texts)
+
+    embeddings = pandas.DataFrame(output)
+    embeddings.to_csv("embeddings.csv", index=False)
+
+if __name__ == "__main__":
+    main()
+